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rayenamer

MCP Chat

by rayenamer
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Python

Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: edit_document modifies content by replacing strings, while read_doc_contents retrieves content without modification. There is no overlap or ambiguity between these operations.

    Naming Consistency3/5

    The naming is mixed: edit_document uses verb_noun format, but read_doc_contents uses a less consistent verb_noun_noun style. Both are readable, but they don't follow a unified pattern.

    Tool Count3/5

    With only 2 tools, the server feels thin for a 'Chat' domain, which typically involves more operations like creating, listing, or deleting documents. However, the tools cover basic read/write functionality.

    Completeness2/5

    For a document management or chat system, there are significant gaps: no tools to create, list, delete, or search documents, and no support for chat-specific features like sending messages or managing conversations. The surface is severely incomplete for the implied domain.

  • Average 3.2/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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  • This repository includes a README.md file.

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  "maintainers": [
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  ]
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool reads and returns content as a string, which implies a read-only operation, but doesn't cover critical aspects like error handling, performance, rate limits, or what happens if the document doesn't exist. This leaves significant gaps for a tool with no annotation support.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, efficient sentence that directly states the tool's purpose and output. It is front-loaded with the core action and resource, with no wasted words or redundant information, making it highly concise and well-structured.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's low complexity (one parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavioral traits and usage guidelines. Without annotations or output schema, more context on errors or return format would improve completeness, but it's not severely lacking for such a simple tool.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema has 100% description coverage, fully documenting the single 'doc_id' parameter. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. This meets the baseline of 3 since the schema handles the heavy lifting.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the action ('Read the contents') and resource ('of a document'), specifying what the tool does. It distinguishes from the sibling 'edit_document' by focusing on reading rather than editing. However, it doesn't specify if it reads the entire document or partial content, which prevents a perfect score.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling 'edit_document' or any other tools, nor does it specify prerequisites like document accessibility or permissions. Usage context is implied but not explicitly stated.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description carries the full burden. It implies a mutation operation ('edit'), but lacks details on permissions, side effects (e.g., whether edits are reversible), error handling, or rate limits. This is inadequate for a tool that modifies content.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, direct sentence with no wasted words, clearly front-loading the core functionality. It efficiently communicates the tool's purpose without unnecessary elaboration.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a mutation tool with no annotations and no output schema, the description is insufficient. It lacks critical behavioral details (e.g., what happens on failure, response format) and does not compensate for the absence of structured data, leaving gaps in understanding.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 100%, so the schema fully documents all three parameters. The description adds no additional meaning beyond what the schema provides, such as examples or edge cases, but does not contradict it. Baseline 3 is appropriate.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the action ('edit a document') and the specific method ('by replacing a string in the documents content with a new string'), distinguishing it from the sibling tool 'read_doc_contents' which presumably only reads content. It uses specific verbs and resources without being tautological.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool versus alternatives, such as the sibling 'read_doc_contents'. It does not mention prerequisites, exclusions, or contextual cues for selection, leaving usage decisions unclear.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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